SHM deformation monitoring for high-speed rail track slabs and Bayesian change point detection for the measurements

Abstract

Fiber Bragg grating (FBG) technology has significant advantages in anti-electromagnetic interference and is suitable for structural health monitoring (SHM) in high-speed rail (HSR). In this paper, a deformation monitoring system based on FBG technology is designed for HSR track slab. Regarding to the possible large deformation of the track slab, a Bayesian change point detection (CPD) method is adopted to detect change points of the monitoring data sequence and the posterior probability distribution of change points can also be derived. The Bayesian CPD algorithm can directly sample and simulate from the posterior probability distribution of change point to determine corresponding parameters of regression model, as well as yielding uncertainty evaluation. The designed SHM system was implemented on a segment of HSR track on the Lanzhou-Xinjiang line in China to carry out the measurement of track slab deformation. Results shows that the designed SHM can resist strong electromagnetic interference in the operation environment of high-speed rail and versine deformation monitoring data over two months for the target track segment can be extracted based on SHM measurement. Through posterior distribution information of the Bayesian CPD method, change points which have the highest posterior probability can be identified for the versine deformation of HSR track slabs, which is essential for investigating the cause of the change point and providing maintenance plan in time.